class_docstr: Class that collects and stores information and statistics on issues found in a dataset.
  
  Parameters
  ----------
  data :
      The data object for which the issues are being collected.
  strategy :
      Strategy used for processing info dictionaries.
  
  Attributes
  ----------
  issues : pd.DataFrame
      Stores information about each individual issue found in the data,
      on a per-example basis.
  issue_summary : pd.DataFrame
      Summarizes the overall statistics for each issue type.
  info : dict
      A dictionary that contains information and statistics about the data and each issue type.
docstr: Summarize the issues found in dataset of a particular type,
  including how severe this type of issue is overall across the dataset.
  
  Parameters
  ----------
  issue_name :
      Name of the issue type to summarize. If `None`, summarizes each of the different issue types previously considered in the audit.
  
  Returns
  -------
  issue_summary :
      DataFrame where each row corresponds to a type of issue, and columns quantify:
      the number of examples in the dataset estimated to exhibit this type of issue,
      and the overall severity of the issue across the dataset (via a numeric quality score where lower values indicate that the issue is overall more severe).
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code: def get_issue_summary(
      self, issue_name: Optional[str] = None
  ) -> pd.DataFrame:
      """Summarize the issues found in dataset of a particular type,
      including how severe this type of issue is overall across the dataset.
  
      Parameters
      ----------
      issue_name :
          Name of the issue type to summarize. If `None`, summarizes each of the different issue types previously considered in the audit.
  
      Returns
      -------
      issue_summary :
          DataFrame where each row corresponds to a type of issue, and columns quantify:
          the number of examples in the dataset estimated to exhibit this type of issue,
          and the overall severity of the issue across the dataset (via a numeric quality score where lower values indicate that the issue is overall more severe).
      """
      if self.issue_summary.empty:
          raise ValueError(
              "No issues found in the dataset. "
              "Call `find_issues` before calling `get_issue_summary`."
          )
  
      if issue_name is None:
          return self.issue_summary
  
      row_mask = self.issue_summary["issue_type"] == issue_name
      if not any(row_mask):
          raise ValueError(
              f"Issue type {issue_name} not found in the summary."
          )
      return self.issue_summary[row_mask].reset_index(drop=True)
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling the DataIssues obj's underlying member method code with example usage: obj.get_issue_summary(issue_name) and returns: self, self